Binning-enhanced defect detection method for three-dimensional wafer structures

ABSTRACT

Location-based binning can separate defects on different rows of channel holes in a 3D NAND structure to corresponding bins. A one-dimensional projection of an image is generated and a one-dimensional curve is formed. A mask is generated from the one-dimensional curve. Defects in the image are detected using the mask and location-based binning is performed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to the provisional patent applicationfiled Sep. 4, 2020 and assigned U.S. App. No. 63/074,487, the disclosureof which is hereby incorporated by reference.

FIELD OF THE DISCLOSURE

This disclosure relates to defect detection on semiconductor wafers.

BACKGROUND OF THE DISCLOSURE

Evolution of the semiconductor manufacturing industry is placing greaterdemands on yield management and, in particular, on metrology andinspection systems. Critical dimensions continue to shrink, yet theindustry needs to decrease time for achieving high-yield, high-valueproduction. Minimizing the total time from detecting a yield problem tofixing it maximizes the return-on-investment for a semiconductormanufacturer.

Fabricating semiconductor devices, such as logic and memory devices,typically includes processing a semiconductor wafer using a large numberof fabrication processes to form various features and multiple levels ofthe semiconductor devices. For example, lithography is a semiconductorfabrication process that involves transferring a pattern from a reticleto a photoresist arranged on a semiconductor wafer. Additional examplesof semiconductor fabrication processes include, but are not limited to,chemical-mechanical polishing (CMP), etching, deposition, and ionimplantation. An arrangement of multiple semiconductor devicesfabricated on a single semiconductor wafer may be separated intoindividual semiconductor devices.

Inspection processes are used at various steps during semiconductormanufacturing to detect defects on wafers to promote higher yield in themanufacturing process and, thus, higher profits. Inspection has alwaysbeen an important part of fabricating semiconductor devices such asintegrated circuits (ICs). However, as the dimensions of semiconductordevices decrease, inspection becomes even more important to thesuccessful manufacture of acceptable semiconductor devices becausesmaller defects can cause the devices to fail. For instance, as thedimensions of semiconductor devices decrease, detection of defects ofdecreasing size has become necessary because even relatively smalldefects may cause unwanted aberrations in the semiconductor devices.

As demand for smaller semiconductor devices continues to increase, ithas become more difficult to shrink semiconductor devices, such asmemory, due to rapidly increasing costs associated with lithography andmultiple process steps associated with pitch splitting techniques.Vertical memory, such as 3D NAND memory, appears to be a promisingdirection for increasing memory density. Implementation of 3D NANDincludes building transistors (bits) vertically, rather than orientingmemory structures in a planar manner. Increasing the number of bits canbe achieved with fewer process steps, relaxed lithography sizes, andlower manufacturing costs as compared with the planar approach.

3D NAND has layers with holes. Semiconductor manufacturers are usuallyconcerned which row of these channel holes includes a defect. A defectat a hole closer to a channel can be more problematic. Many inspectionsystems lack the resolution to determine where the defect is located.There is a demand for improved semiconductor wafer inspection systemsfor implementation with vertical semiconductor devices, such as 3D NANDmemory or other vertical stacks. Previous methods, such as Image-basedSuperCell (IBS), were used to find defects on 3D NAND structures.However, IBS typically cannot handle local and global gray level (GL)variations, which introduced separation error. Noise can affect theresults.

Therefore, improved methods and systems for defect detection are needed.

BRIEF SUMMARY OF THE DISCLOSURE

A method is provided in a first embodiment. The method includesreceiving an image at a processor. The image is of a three-dimensionalstructure of a semiconductor wafer and can be generated by a broad-bandplasma inspection system. A one-dimensional projection of the image isgenerated using the processor thereby forming a one-dimensional curve. Amask is generated from the one-dimensional curve of the image using theprocessor. Defects are detected on the image with the mask using theprocessor. Location-based binning of the defects is performed using theprocessor.

The three-dimensional structure may be a three-dimensional NANDstructure.

Generating the mask can include performing an auto-correlation of theone-dimensional curve using the processor thereby determining a periodand performing auto-convolution and arbitration of the period using theprocessor thereby determining a trench center. The trench center can beused as a reference. Trench, edge hole, transition hole, and center holeregions can be determined in the mask image using the processor.

The defects can be detected amongst pixels in a region of the mask.Detecting the defects further can include extracting a patch around alocation of one of the defects. The method can further includedetermining a distance to a neighboring trench center using theprocessor. The location-based binning may be adistance-to-trench-center.

The location-based binning can separate the defects on different rows ofchannel holes to corresponding bins.

A non-transitory computer readable medium can store a program configuredto instruct a processor to execute the method of the first embodiment.

A system is provided in a second embodiment. The system includes a stageconfigured to hold a semiconductor wafer; a light source configured todirect a beam of light at the semiconductor wafer on the stage; adetector configured to receive reflected light from the semiconductorwafer on the stage; and a processor in electronic communication with thedetector. The light source may be a broad-band plasma source. Thedetector is configured to receive an image of the semiconductor wafer;generate a one-dimensional projection of the image thereby forming aone-dimensional curve; generate a mask from the one-dimensional curve ofthe image; detect defects on the image with the mask; and performlocation-based binning of the defects.

Generating the mask can include performing an auto-correlation of theone-dimensional curve thereby determining a period and performingauto-convolution and arbitration of the period thereby determining atrench center.

The trench center can be used as a reference. Trench, edge hole,transition hole, and center hole regions can be determined in the maskimage.

The defects can be detected amongst pixels in a region of the mask.Detecting the defects can further include extracting a patch around alocation of one of the defects. The method can further includedetermining a distance to a neighboring trench center. Thelocation-based binning can be a distance-to-trench-center.

The location-based binning can separate the defects on different rows ofchannel holes to corresponding bins.

DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the nature and objects of the disclosure,reference should be made to the following detailed description taken inconjunction with the accompanying drawings, in which:

FIG. 1 is an exemplary diagram of a 3D NAND structure;

FIG. 2A illustrates a frame image;

FIG. 2B illustrates a corresponding horizontal projection to FIG. 2A;

FIG. 2C illustrates a corresponding mask image to FIG. 2A;

FIG. 3 is a flowchart of a method embodiment in accordance with thepresent disclosure;

FIG. 4 illustrates an example of programmed missing defects;

FIG. 5 illustrates histograms comparing a method embodiment of thepresent disclosure and IBS; and

FIG. 6 is a block diagram of a system in accordance with the presentdisclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Although claimed subject matter will be described in terms of certainembodiments, other embodiments, including embodiments that do notprovide all of the benefits and features set forth herein, are alsowithin the scope of this disclosure. Various structural, logical,process step, and electronic changes may be made without departing fromthe scope of the disclosure. Accordingly, the scope of the disclosure isdefined only by reference to the appended claims.

A broad-band plasma (BBP) inspection system, laser inspection system, orother optical inspection systems can be used for 3D NAND defectdetection. To improve sensitivity, semiconductor manufacturers may needto differentiate defects located on trenches and different rows ofchannel holes instead of merely reporting all defects in the sameregion. Missing a hole near a trench usually has a bigger impact thanparticles on trenches, while center holes are dummy holes and generallyless critical than other holes.

Embodiments disclosed herein use of a-priori information (e.g.,periodicity, symmetry, etc.) to overcome influences of GL variations andauto-focus issue. An arbitration method can be implemented to correcttrench location identification. Cross-frame borrowing logic can be usedto tolerate global GL variation. Image pixels can be segmented based ontheir distances to trench center. Individual sensitivity can be appliedto each segmentation for defect detection. The distance from defectlocation to trench center can be calculated for defect binning in therecipe. Compared to IBS, the embodiments disclosed herein are robust towafer process variation and noise and also can tolerate some auto-focusissues. Separation purity also can be better than IBS. IBS separates theimages based on the neighboring pixels and the local gray levelvariation will impact the binning performance. Embodiments disclosedherein use the pixels from the whole care area, which is more robust totolerate the local variation.

An exemplary 3D NAND structure image is shown in FIG. 1 with variousfeatures. Bins are illustrated, with Bin 0 representing the background.Each row of holes may be 100 nm to 200 nm in height. 3D NAND images aretypically close to being horizontally uniform while being verticallyperiodic and symmetric as shown in the frame image in FIG. 2A. The imagein FIG. 2A may be provided by a BBP inspection system or otherinspection system. The horizontal projection of the image in FIG. 2A canbe used to produce a periodic and symmetric curve as shown in FIG. 2B,which results in the mask image of FIG. 2C. This structure can increasethe complexity of defect detection. Using embodiments disclosed herein,cross-correlation can help to identify the pitch and trench center.Image pixels can be segmented based on their distances to trench center.Individual sensitivity can be applied to each segmentation for defectdetection. The distance from defect location to trench center can becalculated for quantitative defect binning.

FIG. 3 is a flowchart of a method 100. Some or all of the steps of themethod 100 can use a processor.

An image 101 is used in the method 100. The image 101 is of athree-dimensional structure of a semiconductor wafer, such as a 3D NANDstructure. The exemplary image, such as that shown in FIG. 1, may befrom an electron beam inspection system or another type of inspectionsystem

At 102, a one-dimensional projection of the image, such as that shown inFIG. 2A, is generated thereby forming a one-dimensional curve, such asthat shown in FIG. 2B. The one-dimensional projection accumulates allpixels along a dimension to determine a gray level distribution. Theone-dimensional curve represents the gray level of the one-dimensionalprojection.

In an instance, the algorithm can take an average of the gray level ofall pixels parallel to the trench in the image to generate theone-dimensional projection. The one-dimensional projection can beconverted to the one-dimensional curve by using an average value output.Each row has one average value output. The values from all rows can formthe one-dimensional curve.

At 103, an auto-correlation of the one-dimensional curve is performedthereby determining a period. An example of the auto-correlationfunction is in Eq. 6. The auto-correlation of one-dimensional curve candetermine the period length of unique trench-hole pitch.Auto-correlation of one-dimensional curve also can provide trench centercandidates.

In an instance, auto-correlation determines a pitch from an originalprofile to an offset profile. The original profile can be theone-dimensional projection profile. The offset profile can be theprofile R_(x)[k]. A peak value can be examined and a peak-to-peak valueis determined. This may include normalized cross-correlation (NCC).

At 104, auto-convolution and arbitration of the period is performedthereby determining a trench center. The arbitration method can be usedto determine whether the candidates are trench centers or center holerows. For example, a profile can be flipped to find a trench centerduring auto-convolution. Arbitration can use the dark or light peak as acenter, which can be based on information from the semiconductormanufacturer. For example, a user can select a dark peak or light peakin a user interface.

An example of auto-correlation 103 and auto-convolution 104 is provided.A real-valued discrete signal x[n] is T-periodic if there exists apositive integer T such that for every n ∈Z, x[n]=x[n+T]. The signalx[n] is M-symmetric if there exists an integer M such that for every n∈Z, x[n]=x[M−n]. In this case, M/2 is one of the symmetric centers andis not necessarily unique.

If a signal is both T-periodic and M-symmetric, then it also is(M+jT)-symmetric, where j is an arbitrary integer. For a fixed integerj, x[n]=x[M−n]=x[M+jT−n] for all n ∈Z. Therefore, x[n] is also(M+jT)-symmetric. This means that a periodic and symmetric signalcontain a series of symmetric center with half-cycle spacing.

The autocorrelation function at lag k for a discrete signal x[n] can bedefined as R_(x)[k]=Σ_(nϵz)(x[n])(x[n+k]). The autoconvolution functionat lag k for a discrete signal x[n] can be defined asV_(x)[k]=Σ_(nϵz)(x[n])(x[k−n]), which can be viewed as thecross-correlation of x[n] with its reversion x[−n]. Thus, the followingequations apply.

R _(x)[k+T]=Σ_(n∈Z)(x[n])(x[n k+T])=Σ_(n∈Z)(x[n])(x[n k])=R_(x)[k]  (Eq. 1)

V _(x)[k+T]=Σ_(n∈Z)(x[n])(x[k+T−n])=V _(x)[k]  (Eq. 2)

If x[n] is M-symmetric, then Vx[k] achieves global maximum at k=M. UsingEq. 3

(x[n])(x[k−n])=½(x ²[n]+x ²[k−n]−(x[n]−x[k−n])²)  (Eq. 3)

then V_(x)[k] cannot exceed V_(x) [M] for any k. This is shown in Eq. 4.

V _(x)[k]=Σ_(n∈Z)(x[n])(x[k−n])=Σ_(n∈Z)(x²[n])−Σ_(n∈Z)(x[n]−x[k−n])²=Σ_(n∈Z)(x[n]x[M−n])−½Σ_(n∈Z)(x[n]−x[k−n])²≤V _(M)[k]  (Eq. 4)

So the symmetric center of the signal can be determined from peaks inits autoconvolution function.

If x[n] is both T-periodic and M-symmetric, then the autoconvolutionfunction Vx[k] achieves global maximum at k=M+jT for every j∈Z. This canbe used for pitch detection for a three-dimensional structure.

With the period and trench centers identified, a mask image is generatedfrom the one-dimensional curve of the image at 106, such as that in FIG.2C. The mask image can be based on predefined widths of trench area andeach hole region. Thus, the trench center can be used as a reference.Trench, edge hole, transition hole, and center hole regions can bedetermined in the mask image, as shown in FIG. 1. The transition holesand center holes are outlined with dashed lines in FIG. 1. The edgeholes are an outside row of a group of holes closest to the trench,center holes are at a center of a group of holes, and the transitionholes are between the edge holes and the center holes. In an instance,after a trench center is determined, within a period, the trench centeris segmented into subregions based on sensitivity thresholds to generatethe mask image.

In an instance, the semiconductor manufacturer can specify sensitivityin different regions for the mask based on the semiconductor structureor other design. Thus, the mask can indicate a region of pixels withparticular sensitivity. A semiconductor manufacturer can set asensitivity on a BBP inspection system for each subregion such that eachsubregion can use an independent threshold.

The pixels of the image from 101 can be segmented based on the mask from106 for detection of individual segmentation.

Defects on the image are detected with the mask at 107. For example,defects can be detected amongst pixels in a region of the mask withdesired sensitivity. Detecting the defects can include extracting apatch around a location of one of the defects. In an example, the patchsize is 32×32 pixels for a BBP inspection system. A distance between adefect to a neighboring trench center can be determined. After thetrench center is calculated, and for each defect, the defect peaklocation can also be found. The distance is the difference between thesetwo values. This distance can be used for defect binning. In aninstance, a local maximal in a patch difference image can represent adefect.

Defect locations can be calculated at 108. Location-based binning can beperformed at 109 based on a distribution. The results of thelocation-based binning can be turned into a histogram, such as thatshown in FIG. 3. In an example, the distribution of defects with respectto distance-to-trench-center is the final binning result. This can beused to determine locations of the defects of interest.

The operation is further described in the example below. Let x[n] be areal-valued discrete signal. This is T-periodic if there exists apositive integer T s.t. x[n]=x[n+T] for every n∈

and this is M-symmetric if there exists an integer M s.t. x[n]=x[M−n]for every n∈

. In this case, M/2 is one of the symmetric centers (not necessarilyunique). It can be verified if a signal is both T-periodic andM-symmetric. It also is (M+jT)-symmetric for every j∈

. As a result, a periodic and symmetric signal contains a series ofsymmetric centers with half-period spacing.

For auto-correlation and auto-convolution, the cross-correlationfunction can be used to detect the periodicity and symmetry. Note that aperiodic and symmetric signal will overlay with itself at every multipleof period. Therefore, the cross-correlation function of the signal withitself will reach maxima at a series of points with one-period spacing.This type of cross-correlation function is referred to as theauto-correlation function. Moreover, the signal will also overlay withits reversion at every multiple of period. Thus, the cross-correlationfunction of the signal with its reversion will contain a series ofpeaks, each of which corresponds to a symmetric center of the originalsignal. This type of cross-correlation function is defined as theauto-convolution function. In summary, the periodicity and symmetry canbe determined from the auto-correlation and auto-convolution functionsrespectively.

Suppose that the projection data is given by x[0], x[1], . . . ,x[N−1].The mean and variance can be defined as follows in Eq. 5.

$\begin{matrix}{{\mu = {\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;{x\lbrack n\rbrack}}}},{\sigma^{2} =}} & \left( {{Eq}.\mspace{14mu} 5} \right)\end{matrix}$

${\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}\;\left( {{x\lbrack n\rbrack} - \mu} \right)^{2}}},$

An estimate of the auto-correlation and auto-convolution at a positivelag k may be obtained using Eq. 6.

$\begin{matrix}{{{R_{x}\lbrack k\rbrack} = {\frac{1}{N - k}{\sum\limits_{n = 0}^{N - 1 - k}\;{\left( {{x\lbrack n\rbrack} - \mu} \right)\left( {{x\left\lbrack {n + k} \right\rbrack} - \mu} \right)}}}},{{V_{x}\lbrack k\rbrack} = {\frac{1}{k + 1}{\sum\limits_{n = 0}^{k}\;{\left( {{x\lbrack n\rbrack} - \mu} \right){\left( {{x\left\lbrack {k - n} \right\rbrack} - \mu} \right).}}}}}} & \left( {{Eq}.\mspace{14mu} 6} \right)\end{matrix}$

The expression for negative lag k can be defined likewise. It may beconvenient to normalize the auto-correlation R_(x)[k] andauto-convolution V_(x)[k] functions using Eq. 7.

$\begin{matrix}{{{r_{x}\lbrack k\rbrack} = \frac{R_{x}\lbrack k\rbrack}{\sigma^{2}}},{{v_{x}\lbrack k\rbrack} = {\frac{V_{x}\lbrack k\rbrack}{\sigma^{2}}.}}} & \left( {{Eq}.\mspace{14mu} 7} \right)\end{matrix}$

The terms auto-correlation and auto-convolution can represent thenormalized version.

The periodicity and symmetry can be inferred from the auto-correlationand auto-convolution functions. All the peaks which are higher than agiven threshold value can be initially found. The pitch can then beidentified as the average interval of the peaks.

With the pitch T obtained, the trench center can be further determined.Suppose that the peaks of auto-convolution function occur at m₀<m₁< . .. <M_(L−1). Due to the presence of wafer noise, some of the expectedpeak may be missing. M can be found such that M+j_(l)T≈m_(l),l=0,1, . .. ,L−1, for some unknown j_(l)∈

. Therefore, the following optimization problem with respect to M,j₀, .. . ,j_(L−1).

$\begin{matrix}{{\min{\sum\limits_{l = 0}^{L - 1}\;{\left( {M + {j_{l}T} - m_{l}} \right)^{2}\mspace{14mu}{s.t.\mspace{14mu} j_{0}}}}},\cdots\;,{j_{L - 1} \in {\mathbb{Z}}}} & \left( {{Eq}.\mspace{14mu} 8} \right)\end{matrix}$

Without a loss of generality, M may be restricted in the range [−T/2,T/2). The optimal solution M* of the problem Eq. 8 can belong to thefollowing candidate set

${\left\{ {\overset{\_}{q},{\overset{\_}{q} - \Delta},\cdots\;,{\overset{\_}{q} - {L\;\Delta}}} \right\}\bigcap\left\lbrack {{{- T}\text{/}2},{T\text{/}2}} \right)},{{{where}\mspace{14mu}\overset{\_}{q}} = {\sum\limits_{l = 0}^{L - 1}\; q_{l}}},{\Delta = \frac{T}{L}},$

and q_(l)∈[0, T) is the remainder of m_(l) with respect to T.Consequently, the value of objective function on the candidate set canbe compared and the minimizer M* can be found.

As shown in 3D NAND images, half-pitch ambiguity can exist on the trenchcenter, either in bright center or dark center. An arbitration may berequired to determine the correct trench center. It can be difficult todetermine the trench polarity from the projection data alone. There areat least three ways for arbitration. A first is using brightness fromobservation. A second is to use horizontal variance because a holeregion typically has more wafer noise. A third is to predefine atemplate to match the wafer pattern. Since there can be layer-to-layer,wafer-to-wafer, and die-to-die variations, an empirical choice ofarbitration method may be applied to each specific layer.

The obtained trench center can be used as reference. Trench, edge hole,transition hole, and center hole regions can be filled one by one withcorresponding percentages repeatedly in the mask image. Using the maskimage, segmented multi-die automatic thresholding (MDAT) detection isperformed to detect defects for each segmentation.

When there are global GL variations, some frames may suffer from wrongpitch and trench center. A borrow logic may be implemented to get pitchand trench center values from other frames. Thus, values of period andtrench centers can be borrowed from neighboring frames. Frame to frameoffsets may be considered during the borrowing.

In an experiment, the method 100 was tested on a wafer using a BBPinspection system. Performance was compared to IBS. There are programmedhole missing defects on the wafer as shown in FIG. 4. Each dot in FIG. 4is a channel hole. Missing defects exist in row 1 to row 5, which areshown with the hollow circles in FIG. 4. The objective is todifferentiate defects from distinct rows.

The distance of defect to trench center is calculated as an attribute ofdefects for binning in the prototype development. The histograms in FIG.5 show that the embodiments disclosed herein (“new binning algo”) canfind clear cutline to separate two different types of defects, whilethere is a large overlap between distribution of two types of defects byusing IBS. The overall binning accuracy is improved using theembodiments disclosed herein due to the clear cutline on the histogram.

Using a-priori information (e.g. periodicity, symmetry, etc.) of waferlayout can overcome influences of GL variations and auto-focus issues.Embodiments disclosed herein can separate defects on different rows ofchannel holes to corresponding bins correctly and help semiconductormanufacturers achieve enhanced sensitivity tuning and precisedefectivity monitoring for better control of wafer yield.

One embodiment of a system 200 is shown in FIG. 6. The system 200includes optical based subsystem 201. In general, the optical basedsubsystem 201 is configured for generating optical based output for aspecimen 202 by directing light to (or scanning light over) anddetecting light from the specimen 202. In one embodiment, the specimen202 includes a wafer. The wafer may include any wafer known in the art.In another embodiment, the specimen 202 includes a reticle. The reticlemay include any reticle known in the art.

In the embodiment of the system 200 shown in FIG. 6, optical basedsubsystem 201 includes an illumination subsystem configured to directlight to specimen 202. The illumination subsystem includes at least onelight source. For example, as shown in FIG. 6, the illuminationsubsystem includes light source 203. In one embodiment, the illuminationsubsystem is configured to direct the light to the specimen 202 at oneor more angles of incidence, which may include one or more obliqueangles and/or one or more normal angles. For example, as shown in FIG.6, light from light source 203 is directed through optical element 204and then lens 205 to specimen 202 at an oblique angle of incidence. Theoblique angle of incidence may include any suitable oblique angle ofincidence, which may vary depending on, for instance, characteristics ofthe specimen 202.

The optical based subsystem 201 may be configured to direct the light tothe specimen 202 at different angles of incidence at different times.For example, the optical based subsystem 201 may be configured to alterone or more characteristics of one or more elements of the illuminationsubsystem such that the light can be directed to the specimen 202 at anangle of incidence that is different than that shown in FIG. 6. In onesuch example, the optical based subsystem 201 may be configured to movelight source 203, optical element 204, and lens 205 such that the lightis directed to the specimen 202 at a different oblique angle ofincidence or a normal (or near normal) angle of incidence.

In some instances, the optical based subsystem 201 may be configured todirect light to the specimen 202 at more than one angle of incidence atthe same time. For example, the illumination subsystem may include morethan one illumination channel, one of the illumination channels mayinclude light source 203, optical element 204, and lens 205 as shown inFIG. 6 and another of the illumination channels (not shown) may includesimilar elements, which may be configured differently or the same, ormay include at least a light source and possibly one or more othercomponents such as those described further herein. If such light isdirected to the specimen at the same time as the other light, one ormore characteristics (e.g., wavelength, polarization, etc.) of the lightdirected to the specimen 202 at different angles of incidence may bedifferent such that light resulting from illumination of the specimen202 at the different angles of incidence can be discriminated from eachother at the detector(s).

In another instance, the illumination subsystem may include only onelight source (e.g., light source 203 shown in FIG. 6) and light from thelight source may be separated into different optical paths (e.g., basedon wavelength, polarization, etc.) by one or more optical elements (notshown) of the illumination subsystem. Light in each of the differentoptical paths may then be directed to the specimen 202. Multipleillumination channels may be configured to direct light to the specimen202 at the same time or at different times (e.g., when differentillumination channels are used to sequentially illuminate the specimen).In another instance, the same illumination channel may be configured todirect light to the specimen 202 with different characteristics atdifferent times. For example, in some instances, optical element 204 maybe configured as a spectral filter and the properties of the spectralfilter can be changed in a variety of different ways (e.g., by swappingout the spectral filter) such that different wavelengths of light can bedirected to the specimen 202 at different times. The illuminationsubsystem may have any other suitable configuration known in the art fordirecting the light having different or the same characteristics to thespecimen 202 at different or the same angles of incidence sequentiallyor simultaneously.

In one embodiment, light source 203 may include a BBP source. In thismanner, the light generated by the light source 203 and directed to thespecimen 202 may include broadband light. However, the light source mayinclude any other suitable light source such as a laser. The laser mayinclude any suitable laser known in the art and may be configured togenerate light at any suitable wavelength or wavelengths known in theart. In addition, the laser may be configured to generate light that ismonochromatic or nearly-monochromatic. In this manner, the laser may bea narrowband laser. The light source 203 may also include apolychromatic light source that generates light at multiple discretewavelengths or wavebands.

Light from optical element 204 may be focused onto specimen 202 by lens205. Although lens 205 is shown in FIG. 6 as a single refractive opticalelement, it is to be understood that, in practice, lens 205 may includea number of refractive and/or reflective optical elements that incombination focus the light from the optical element to the specimen.The illumination subsystem shown in FIG. 6 and described herein mayinclude any other suitable optical elements (not shown). Examples ofsuch optical elements include, but are not limited to, polarizingcomponent(s), spectral filter(s), spatial filter(s), reflective opticalelement(s), apodizer(s), beam splitter(s) (such as beam splitter 213),aperture(s), and the like, which may include any such suitable opticalelements known in the art. In addition, the optical based subsystem 201may be configured to alter one or more of the elements of theillumination subsystem based on the type of illumination to be used forgenerating the optical based output.

The optical based subsystem 201 may also include a scanning subsystemconfigured to cause the light to be scanned over the specimen 202. Forexample, the optical based subsystem 201 may include stage 206 on whichspecimen 202 is disposed during optical based output generation. Thescanning subsystem may include any suitable mechanical and/or roboticassembly (that includes stage 206) that can be configured to move thespecimen 202 such that the light can be scanned over the specimen 202.In addition, or alternatively, the optical based subsystem 201 may beconfigured such that one or more optical elements of the optical basedsubsystem 201 perform some scanning of the light over the specimen 202.The light may be scanned over the specimen 202 in any suitable fashionsuch as in a serpentine-like path or in a spiral path.

The optical based subsystem 201 further includes one or more detectionchannels. At least one of the one or more detection channels includes adetector configured to detect light from the specimen 202 due toillumination of the specimen 202 by the subsystem and to generate outputresponsive to the detected light. For example, the optical basedsubsystem 201 shown in FIG. 6 includes two detection channels, oneformed by collector 207, element 208, and detector 209 and anotherformed by collector 210, element 211, and detector 212. As shown in FIG.6, the two detection channels are configured to collect and detect lightat different angles of collection. In some instances, both detectionchannels are configured to detect scattered light, and the detectionchannels are configured to detect tight that is scattered at differentangles from the specimen 202. However, one or more of the detectionchannels may be configured to detect another type of light from thespecimen 202 (e.g., reflected light).

As further shown in FIG. 6, both detection channels are shown positionedin the plane of the paper and the illumination subsystem is also shownpositioned in the plane of the paper. Therefore, in this embodiment,both detection channels are positioned in (e.g., centered in) the planeof incidence. However, one or more of the detection channels may bepositioned out of the plane of incidence. For example, the detectionchannel formed by collector 210, element 211, and detector 212 may beconfigured to collect and detect light that is scattered out of theplane of incidence. Therefore, such a detection channel may be commonlyreferred to as a “side” channel, and such a side channel may be centeredin a plane that is substantially perpendicular to the plane ofincidence.

Although FIG. 6 shows an embodiment of the optical based subsystem 201that includes two detection channels, the optical based subsystem 201may include a different number of detection channels (e.g., only onedetection channel or two or more detection channels). In one suchinstance, the detection channel formed by collector 210, element 211,and detector 212 may form one side channel as described above, and theoptical based subsystem 201 may include an additional detection channel(not shown) formed as another side channel that is positioned on theopposite side of the plane of incidence. Therefore, the optical basedsubsystem 201 may include the detection channel that includes collector207, element 208, and detector 209 and that is centered in the plane ofincidence and configured to collect and detect light at scatteringangle(s) that are at or close to normal to the specimen 202 surface.This detection channel may therefore be commonly referred to as a “top”channel, and the optical based subsystem 201 may also include two ormore side channels configured as described above. As such, the opticalbased subsystem 201 may include at least three channels (i.e., one topchannel and two side channels), and each of the at least three channelshas its own collector, each of which is configured to collect light atdifferent scattering angles than each of the other collectors.

As described further above, each of the detection channels included inthe optical based subsystem 201 may be configured to detect scatteredlight. Therefore, the optical based subsystem 201 shown in FIG. 6 may beconfigured for dark field (DF) output generation for specimens 202.However, the optical based subsystem 201 may also or alternativelyinclude detection channel(s) that are configured for bright field (BF)output generation for specimens 202. In other words, the optical basedsubsystem 201 may include at least one detection channel that isconfigured to detect light specularly reflected from the specimen 202.Therefore, the optical based subsystems 201 described herein may beconfigured for only DF, only BF, or both DF and BF imaging. Althougheach of the collectors are shown in FIG. 6 as single refractive opticalelements, it is to be understood that each of the collectors may includeone or more refractive optical die(s) and/or one or more reflectiveoptical element(s).

The one or more detection channels may include any suitable detectorsknown in the art. For example, the detectors may includephoto-multiplier tubes (PMTs), charge coupled devices (CCDs), time delayintegration (TDI) cameras, and any other suitable detectors known in theart. The detectors may also include non-imaging detectors or imagingdetectors. In this manner, if the detectors are non-imaging detectors,each of the detectors may be configured to detect certaincharacteristics of the scattered light such as intensity but may not beconfigured to detect such characteristics as a function of positionwithin the imaging plane. As such, the output that is generated by eachof the detectors included in each of the detection channels of theoptical based subsystem may be signals or data, but not image signals orimage data. In such instances, a processor such as processor 214 may beconfigured to generate images of the specimen 202 from the non-imagingoutput of the detectors. However, in other instances, the detectors maybe configured as imaging detectors that are configured to generateimaging signals or image data. Therefore, the optical based subsystemmay be configured to generate optical images or other optical basedoutput described herein in a number of ways.

It is noted that FIG. 6 is provided herein to generally illustrate aconfiguration of an optical based subsystem 201 that may be included inthe system embodiments described herein or that may generate opticalbased output that is used by the system embodiments described herein.The optical based subsystem 201 configuration described herein may bealtered to optimize the performance of the optical based subsystem 201as is normally performed when designing a commercial output acquisitionsystem. In addition, the systems described herein may be implementedusing an existing system (e.g., by adding functionality described hereinto an existing system). For some such systems, the methods describedherein may be provided as optional functionality of the system (e.g., inaddition to other functionality of the system). Alternatively, thesystem described herein may be designed as a completely new system.

The processor 214 may be coupled to the components of the system 200 inany suitable manner (e.g., via one or more transmission media, which mayinclude wired and/or wireless transmission media) such that theprocessor 214 can receive output. The processor 214 may be configured toperform a number of functions using the output. The system 200 canreceive instructions or other information from the processor 214. Theprocessor 214 and/or the electronic data storage unit 215 optionally maybe in electronic communication with a wafer inspection system, a wafermetrology system, or a wafer review system (not illustrated) to receiveadditional information or send instructions. For example, the processor214 and/or the electronic data storage unit 215 can be in electroniccommunication with a scanning electron microscope.

The processor 214, other system(s), or other subsystem(s) describedherein may be part of various systems, including a personal computersystem, image computer, mainframe computer system, workstation, networkappliance, internet appliance, or other device. The subsystem(s) orsystem(s) may also include any suitable processor known in the art, suchas a parallel processor. In addition, the subsystem(s) or system(s) mayinclude a platform with high-speed processing and software, either as astandalone or a networked tool.

The processor 214 and electronic data storage unit 215 may be disposedin or otherwise part of the system 200 or another device. In an example,the processor 214 and electronic data storage unit 215 may be part of astandalone control unit or in a centralized quality control unit.Multiple processors 214 or electronic data storage units 215 may beused.

The processor 214 may be implemented in practice by any combination ofhardware, software, and firmware. Also, its functions as describedherein may be performed by one unit, or divided up among differentcomponents, each of which may be implemented in turn by any combinationof hardware, software and firmware. Program code or instructions for theprocessor 214 to implement various methods and functions may be storedin readable storage media, such as a memory in the electronic datastorage unit 215 or other memory.

If the system 200 includes more than one processor 214, then thedifferent subsystems may be coupled to each other such that images,data, information, instructions, etc. can be sent between thesubsystems. For example, one subsystem may be coupled to additionalsubsystem(s) by any suitable transmission media, which may include anysuitable wired and/or wireless transmission media known in the art. Twoor more of such subsystems may also be effectively coupled by a sharedcomputer-readable storage medium (not shown).

The processor 214 may be configured to perform a number of functionsusing the output of the system 200 or other output. For instance, theprocessor 214 may be configured to send the output to an electronic datastorage unit 215 or another storage medium. The processor 214 may beconfigured according to any of the embodiments described herein. Theprocessor 214 also may be configured to perform other functions oradditional steps using the output of the system 200 or using images ordata from other sources.

Various steps, functions, and/or operations of system 200 and themethods disclosed herein are carried out by one or more of thefollowing: electronic circuits, logic gates, multiplexers, programmablelogic devices, ASICs, analog or digital controls/switches,microcontrollers, or computing systems. Program instructionsimplementing methods such as those described herein may be transmittedover or stored on carrier medium. The carrier medium may include astorage medium such as a read-only memory, a random access memory, amagnetic or optical disk, a non-volatile memory, a solid state memory, amagnetic tape, and the like. A carrier medium may include a transmissionmedium such as a wire, cable, or wireless transmission link. Forinstance, the various steps described throughout the present disclosuremay be carried out by a single processor 214 or, alternatively, multipleprocessors 214. Moreover, different sub-systems of the system 200 mayinclude one or more computing or logic systems. Therefore, the abovedescription should not be interpreted as a limitation on the presentdisclosure but merely an illustration.

In an instance, the processor 214 is in communication with the system200. The location-based binning using the processor 214 can separate thedefects on different rows of channel holes to corresponding bins. Theprocessor 214 is configured to receive an image of the semiconductorwafer; generate a one-dimensional projection of the image therebyforming a one-dimensional curve; generate a mask from theone-dimensional curve of the image; detect defects on the image with themask; and perform location-based binning of the defects.

Generating the mask can include performing an auto-correlation of theone-dimensional curve thereby determining a period and performingauto-convolution and arbitration of the period thereby determining atrench center. The trench center can be used as a reference. Trench,edge hole, transition hole, and center hole regions can be determined inthe mask image.

The defects can be detected amongst pixels in a region of the mask.Detecting the defects can further include extracting a patch around alocation of one of the defects. A distance to a neighboring trenchcenter can be determined for each defect. For example, this can be adistance from a defect, an edge of a patch, or a center of a patch to aneighboring trench. The location-based binning can be adistance-to-trench-center.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on a controller forperforming a computer-implemented method for defect detection, asdisclosed herein. In particular, as shown in FIG. 6, electronic datastorage unit 215 or other storage medium may contain non-transitorycomputer-readable medium that includes program instructions executableon the processor 214. The computer-implemented method may include anystep(s) of any method(s) described herein, including method 100.

The program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using ActiveX controls, C++ objects,JavaBeans, Microsoft Foundation Classes (MFC), Streaming SIMD Extension(SSE), or other technologies or methodologies, as desired.

Each of the steps of the method may be performed as described herein.The methods also may include any other step(s) that can be performed bythe processor and/or computer subsystem(s) or system(s) describedherein. The steps can be performed by one or more computer systems,which may be configured according to any of the embodiments describedherein. In addition, the methods described above may be performed by anyof the system embodiments described herein.

Although the present disclosure has been described with respect to oneor more particular embodiments, it will be understood that otherembodiments of the present disclosure may be made without departing fromthe scope of the present disclosure. Hence, the present disclosure isdeemed limited only by the appended claims and the reasonableinterpretation thereof.

What is claimed is:
 1. A method comprising: receiving an image at aprocessor, wherein the image is of a three-dimensional structure of asemiconductor wafer; generating a one-dimensional projection of theimage using the processor thereby forming a one-dimensional curve;generating a mask from the one-dimensional curve of the image using theprocessor; detecting defects on the image with the mask using theprocessor; and performing location-based binning of the defects usingthe processor.
 2. The method of claim 1, wherein the image is generatedby a broad-band plasma inspection system.
 3. The method of claim 1,wherein the three-dimensional structure is a three-dimensional NANDstructure.
 4. The method of claim 1, wherein generating the maskincludes: performing an auto-correlation of the one-dimensional curveusing the processor thereby determining a period; and performingauto-convolution and arbitration of the period using the processorthereby determining a trench center.
 5. The method of claim 4, whereinthe trench center is used as a reference, and wherein trench, edge hole,transition hole, and center hole regions are determined in the maskimage using the processor.
 6. The method of claim 1, wherein the defectsare detected amongst pixels in a region of the mask.
 7. The method ofclaim 1, wherein detecting the defects further includes extracting apatch around a location of one of the defects.
 8. The method of claim 7,wherein the method further comprises determining a distance to aneighboring trench center using the processor.
 9. The method of claim 8,wherein the location-based binning is a distance-to-trench-center. 10.The method of claim 1, wherein the location-based binning separates thedefects on different rows of channel holes to corresponding bins.
 11. Anon-transitory computer readable medium storing a program configured toinstruct a processor to execute the method of claim
 1. 12. A systemcomprising: a stage configured to hold a semiconductor wafer; a lightsource configured to direct a beam of light at the semiconductor waferon the stage; a detector configured to receive reflected light from thesemiconductor wafer on the stage; and a processor in electroniccommunication with the detector, wherein the detector is configured to:receive an image of the semiconductor wafer; generate a one-dimensionalprojection of the image thereby forming a one-dimensional curve;generate a mask from the one-dimensional curve of the image; detectdefects on the image with the mask; and perform location-based binningof the defects.
 13. The system of claim 12, wherein the light source isa broad-band plasma source.
 14. The system of claim 12, whereingenerating the mask includes: performing an auto-correlation of theone-dimensional curve thereby determining a period; and performingauto-convolution and arbitration of the period thereby determining atrench center.
 15. The system of claim 14, wherein the trench center isused as a reference, and wherein trench, edge hole, transition hole, andcenter hole regions are determined in the mask image.
 16. The system ofclaim 12, wherein the defects are detected amongst pixels in a region ofthe mask.
 17. The system of claim 12, wherein detecting the defectsfurther includes extracting a patch around a location of one of thedefects.
 18. The system of claim 17, wherein the method furthercomprises determining a distance to a neighboring trench center.
 19. Thesystem of claim 18, wherein the location-based binning is adistance-to-trench-center.
 20. The system of claim 12, wherein thelocation-based binning separates the defects on different rows ofchannel holes to corresponding bins.